Optimized Transpilation

ABSTRACT

System and methods are described to parse input source code and generate a tree representing the input source code, optimize the tree by determining reusable sub-trees of the tree and replacing the reusable sub-trees with variables, and transpile the optimized tree to generate output source code.

TECHNICAL FIELD

One or more implementations relate to transpiling, and more specificallyto optimizing the transpilation of a source input code into a sourceoutput code in a distributed system of a cloud computing environment.

BACKGROUND

“Cloud computing” services provide shared resources, software, andinformation to computers and other devices upon request or on demand.Cloud computing typically involves the over-the-Internet provision ofdynamically scalable and often virtualized resources. Technologicaldetails can be abstracted from end-users, who no longer have need forexpertise in, or control over, the technology infrastructure “in thecloud” that supports them. In cloud computing environments, softwareapplications can be accessible over the Internet rather than installedlocally on personal or in-house computer systems. Some of theapplications or on-demand services provided to end-users can include theability for a user to create, view, modify, store and share documentsand other files.

Computer programs (also called application programs or simplyapplications) may be written to process data and/or control dataprocessing operations in a cloud computing environment. A computerprogram may be compiled or transpiled. Compiling is a general term fortaking source code written in one language and transforming it intoanother language. Transpiling is a specific term for taking input sourcecode written in one language and transforming it into another languageat a similar level of abstraction. The output source code is oftenunderstandable by a human but may need to be evaluated and/orinterpreted on a computing system to perform the encoded actions. Insome situations, the output source code generated by a transpiler isexcessively long and complex, taking up too much processing time togenerate, large data transfers to send to another computing system, andlarge computational loads to evaluate and/or interpret. Thus, bettertechniques of transpilation are desirable.

BRIEF DESCRIPTION OF THE DRAWINGS

The included drawings are for illustrative purposes and serve to provideexamples of possible structures and operations for the disclosedinventive systems, apparatus, methods, and computer-readable storagemedia. These drawings in no way limit any changes in form and detailthat may be made by one skilled in the art without departing from thespirit and scope of the disclosed implementations.

FIG. 1A illustrates an example computing environment of an on-demanddatabase service according to some embodiments.

FIG. 1B illustrates example implementations of elements of FIG. 1A andexample interconnections between these elements according to someembodiments.

FIG. 2A illustrates example architectural components of an on-demanddatabase service environment according to some embodiments.

FIG. 2B illustrates example architectural components of an on-demanddatabase service environment according to some embodiments.

FIG. 3 is a diagrammatic representation of a machine in the exemplaryform of a computer system within which one or more embodiments may becarried out.

FIG. 4 illustrates an example of a user interface to input dataprocessing commands resulting in a formula in some embodiments.

FIG. 5 illustrates an example of an application platform having anoptimizing transpiler to transform an input source code into an outputsource code in some embodiments.

FIG. 6 illustrates an example of a first formula as source input code.

FIG. 7 illustrates an example of an abstract syntax tree (AST) for thefirst formula.

FIG. 8 illustrates an example of the AST for the first formula includinggenerated output source code.

FIG. 9 illustrates an example of a sub-AST according to someembodiments.

FIG. 10 illustrates an example of an optimized AST for the first formulaaccording to some embodiments.

FIG. 11 illustrates an example of an optimized AST for the first formulaof including generated output source code according to some embodiments.

FIG. 12 illustrates an example of a second formula as source input code.

FIG. 13 illustrates an example of an AST for the second formula.

FIG. 14 illustrates examples of sub-ASTs according to some embodiments.

FIG. 15 illustrates an example of an optimized AST for the secondformula according to some embodiments.

FIG. 16 illustrates an example of an optimized AST for the secondformula including generated output source code according to someembodiments.

FIG. 17 is a flow diagram of an example process for optimizing an ASTaccording to some embodiments.

FIG. 18 is a flow diagram of an example process for optimizing an ASTaccording to some embodiments.

FIG. 19 is a flow diagram of an example process for transpiling inputsource code represented by an optimized AST and sub-ASTs according tosome embodiments.

DETAILED DESCRIPTION

Embodiments of the present invention comprise an optimizing transpilersystem to generate, from an input source code written in a firstlanguage, an output source code written in a second language that may beevaluated to perform desired functions or actions in a distributedcomputing system. In an embodiment, the input source code conforms to asyntax for a formula language commercially used by and available fromSalesforce, Inc. In an embodiment, the output source code conforms toJavaScript (JS), a high-level interpreted scripting language defined bythe ECMAScript language specification, 10^(th) edition, June 2019, orearlier editions, published by the European Computer ManufacturersAssociation (ECMA). In other embodiments, other languages for the inputsource code and the output source code may be used. By using the formulalanguage, system administrators and/or users can define formulasdeclaratively, have the formula saved in a database, and then evaluatethe formula at run-time on a client system in the format of the outputsource code (such as JS).

One drawback of some transpiler implementations is that e generatedoutput source code can be excessively long. Long output source coderequires more memory and time to generate, takes longer to transfer toclient systems, and results in long interpretation times on the clientsystems. Embodiments of the present invention generate simpler and moreconcise output source code, resulting in efficiency improvementsthroughout the distributed computing system. In an embodiment, anode-to-node traversal of a data structure called an abstract syntaxtree (AST) used to store the formula is performed where each node of theAST is parsed from the formula (e.g., input source code) to determine ifthe node's contents can be represented as a stored variable. This can becomplicated because not every part of the formula should be evaluated(to prevent against null pointers exceptions, for example). Thus, eachvariable should have a guard to tell whether or not the variable shouldbe evaluated. In embodiments of the present invention, the optimizingtranspiler is aware of the entire formula during formula processing, andthus can process the entire AST for that formula. This means the AST canbe traversed recursively and the optimizing transpiler can handle avariety of different input formulas as the input source code. Inembodiments, parts of the entire formula can be processed separately todetermine optimal ways to combine and/or separate parts of the formulafor better performance and data usage.

FIG. 1A illustrates a block diagram of an example of a cloud computingenvironment 10 in which an on-demand database service can be used inaccordance with some implementations. Environment 10 includes usersystems 12 (e.g., customer's computing systems), a network 14, adatabase system 16 (also referred to herein as a “cloud-based system” ora “cloud computing system”), a processing device 17, an applicationplatform 18, a network interface 20, a tenant database 22 for storingtenant data (such as data sets), a system database 24 for storing systemdata, program code 26 for implementing various functions of the databasesystem 16 (including a visual data cleaning application), and processspace 28 for executing database system processes and tenant-specificprocesses, such as running applications for customers as part of anapplication hosting service. In some other implementations, environment10 may not have all these components or systems, or may have othercomponents or systems instead of, or in addition to, those listed above.In some embodiments, tenant database 22 is a shared storage.

In some implementations, environment 10 is a computing environment inwhich an on-demand database service (such as a distributed searchapplication) exists. An on-demand database service, such as that whichcan be implemented using database system 16, is a service that is madeavailable to users outside an enterprise (or enterprises) that owns,maintains, or provides access to database system 16. As described above,such users generally do not need to be concerned with building ormaintaining database system 16. Instead, resources provided by databasesystem 16 may be available for such users' use when the users needservices provided by database system 16; that is, on the demand of theusers. Some on-demand database services can store information from oneor more tenants into tables of a common database image to form amulti-tenant database system (MTS). The term “multi-tenant databasesystem” can refer to those systems in which various elements of hardwareand software of a database system may be shared by one or more customersor tenants. For example, a given application server may simultaneouslyprocess requests for a large number of customers, and a given databasetable may store rows of data for a potentially much larger number ofcustomers. A database image can include one or more database objects. Arelational database management system (RDBMS) or the equivalent canexecute storage and retrieval of information against the databaseobject(s).

Application platform 18 can be a framework that allows the applicationsof database system 16 to execute, such as the hardware or softwareinfrastructure of database system 16. In some implementations,application platform 18 enables the creation, management and executionof one or more applications developed by the provider of the on-demanddatabase service, users accessing the on-demand database service viauser systems 12, or third-party application developers accessing theon-demand database service via user systems 12.

In some embodiments, application platform 18 includes a transpiler forgenerating output source code (such as JavaScript (JS)) from inputsource code (such as a formula language), the output source code to beevaluated by one or more user systems 12.

In some implementations, database system 16 implements a web-basedcustomer relationship management (CRM) system. For example, in some suchimplementations, database system 16 includes application serversconfigured to implement and execute CRM software applications as well asprovide related data, code, forms, renderable web pages, and documentsand other information to and from user systems 12 and to store to, andretrieve from, a database system related data, objects, and World WideWeb page content. In some MTS implementations, data for multiple tenantsmay be stored in the same physical database object in tenant database22. In some such implementations, tenant data is arranged in the storagemedium(s) of tenant database 22 so that data of one tenant is keptlogically separate from that of other tenants so that one tenant doesnot have access to another tenant's data, unless such data is expresslyshared. Database system 16 also implements applications other than, orin addition to, a CRM application. For example, database system 16 canprovide tenant access to multiple hosted (standard and custom)applications, including a CRM application. User (or third-partydeveloper) applications, which may or may not include CRM, may besupported by application platform 18. Application platform 18 managesthe creation and storage of the applications into one or more databaseobjects and the execution of the applications in one or more virtualmachines in the process space of database system 16.

According to some implementations, each database system 16 is configuredto provide web pages, forms, applications, data, and media content touser (client) systems 12 to support the access by user systems 12 astenants of database system 16. As such, database system 16 providessecurity mechanisms to keep each tenant's data separate unless the datais shared. If more than one MTS is used, they may be located in closeproximity to one another (for example, in a server farm located in asingle building or campus), or they may be distributed at locationsremote from one another (for example, one or more servers located incity A and one or more servers located in city B). As used herein, eachMTS could include one or more logically or physically connected serversdistributed locally or across one or more geographic locations.Additionally, the term “server” is meant to refer to a computing deviceor system, including processing hardware and process space(s), anassociated storage medium such as a memory device or database, and, insome instances, a database application, such as an object-orienteddatabase management system (OODBMS) or a relational database managementsystem (RDBMS), as is well known in the art. It should also beunderstood that “server system”, “server”, “server node”, and “node” areoften used interchangeably herein. Similarly, the database objectsdescribed herein can be implemented as part of a single database, adistributed database, a collection of distributed databases, a databasewith redundant online or offline backups or other redundancies, etc.,and can include a distributed database or storage network and associatedprocessing intelligence.

Network 14 can be or include any network or combination of networks ofsystems or devices that communicate with one another. For example,network 14 can be or include any one or any combination of a local areanetwork (LAN), wide area network (WAN), telephone network, wirelessnetwork, cellular network, point-to-point network, star network, tokenring network, hub network, or other appropriate configuration. Network14 can include a Transfer Control Protocol and Internet Protocol(TCP/IP) network, such as the global internetwork of networks oftenreferred to as the “Internet” (with a capital “I”). The Internet will beused in many of the examples herein. However, it should be understoodthat the networks that the disclosed implementations can use are not solimited, although TCP/IP is a frequently implemented protocol.

User systems 12 (e.g., operated by customers) can communicate withdatabase system 16 using TCP/IP and, at a higher network level, othercommon Internet protocols to communicate, such as the Hyper TextTransfer Protocol (HTTP), Hyper Text Transfer Protocol Secure (HTTPS),File Transfer Protocol (FTP), Apple File Service (AFS), WirelessApplication Protocol (WAP), etc. In an example where HTTP is used, eachuser system 12 can include an HTTP client commonly referred to as a “webbrowser” or simply a “browser” for sending and receiving HTTP signals toand from an HTTP server of the database system 16. Such an HTTP servercan be implemented as the sole network interface 20 between databasesystem 16 and network 14, but other techniques can be used in additionto or instead of these techniques. In some implementations, networkinterface 20 between database system 16 and network 14 includes loadsharing functionality, such as round-robin HTTP request distributors tobalance loads and distribute incoming HTTP requests evenly over a numberof servers. In MTS implementations, each of the servers can have accessto the MTS data; however, other alternative configurations may be usedinstead.

User systems 12 can be implemented as any computing device(s) or otherdata processing apparatus or systems usable by users to access databasesystem 16. For example, any of user systems 12 can be a desktopcomputer, a work station, a laptop computer, a tablet computer, ahandheld computing device, a mobile cellular phone (for example, a“smartphone”), or any other Wi-Fi-enabled device, WAP-enabled device, orother computing device capable of interfacing directly or indirectly tothe Internet or other network. When discussed in the context of a user,the terms “user system,” “user device,” and “user computing device” areused interchangeably herein with one another and with the term“computer.” As described above, each user system 12 typically executesan HTTP client, for example, a web browsing (or simply “browsing”)program, such as a web browser based on the WebKit platform, Microsoft'sInternet Explorer browser, Netscape's Navigator browser, Opera'sbrowser, Mozilla's Firefox browser, Google's Chrome browser, or aWAP-enabled browser in the case of a cellular phone, personal digitalassistant (PDA), or other wireless device, allowing a user (for example,a subscriber of on-demand services provided by database system 16) ofuser system 12 to access, process, and view information, pages, andapplications available to it from database system 16 over network 14.

Each user system 12 also typically includes one or more user inputdevices, such as a keyboard, a mouse, a trackball, a touch pad, a touchscreen, a pen or stylus, or the like, for interacting with a graphicaluser interface (GUI) provided by the browser on a display (for example,a monitor screen, liquid crystal display (LCD), light-emitting diode(LED) display, etc.) of user system 12 in conjunction with pages, forms,applications, and other information provided by database system 16 orother systems or servers. For example, the user interface device can beused to access data and applications hosted database system 16, and toperform searches on stored data, or otherwise allow a user to interactwith various GUI pages that may be presented to a user. As discussedabove, implementations are suitable for use with the Internet, althoughother networks can be used instead of or in addition to the Internet,such as an intranet, an extranet, a virtual private network (VPN), anon-TCP/IP based network, any LAN or WAN or the like.

The users of user systems 12 may differ in their respective capacities,and the capacity of a particular user system 12 can be entirelydetermined by permissions (permission levels) for the current user ofsuch user system. For example, where a salesperson is using a particularuser system 12 to interact with database system 16, that user system canhave the capacities allotted to the salesperson. However, while anadministrator is using that user system 12 to interact with databasesystem 16, that user system can have the capacities allotted to thatadministrator. Where a hierarchical role model is used, users at onepermission level can have access to applications, data, and databaseinformation accessible by a lower permission level user, but may nothave access to certain applications, database information, and dataaccessible by a user at a higher permission level. Thus, different usersgenerally will have different capabilities with regard to accessing andmodifying application and database information, depending on the users'respective security or permission levels (also referred to as“authorizations”).

According to some implementations, each user system 12 and some or allof its components are operator-configurable using applications, such asa browser, including computer code executed using a central processingunit (CPU), such as a Core® processor commercially available from IntelCorporation or the like. Similarly, database system 16 (and additionalinstances of an MTS, where more than one is present) and all of itscomponents can be operator-configurable using application(s) includingcomputer code to run using processing device 17, which may beimplemented to include a CPU, which may include an Intel Core® processoror the like, or multiple CPUs. Each CPU may have multiple processingcores.

Database system 16 includes non-transitory computer-readable storagemedia having instructions stored thereon that are executable by or usedto program a server or other computing system (or collection of suchservers or computing systems) to perform some of the implementation ofprocesses described herein. For example, program code 26 can includeinstructions for operating and configuring database system 16 tointercommunicate and to process web pages, applications (includingvisual data cleaning applications), and other data and media content asdescribed herein. In some implementations, program code 26 can bedownloadable and stored on a hard disk, but the entire program code, orportions thereof, also can be stored in any other volatile ornon-volatile memory medium or device as is well known, such as aread-only memory (ROM) or random-access memory (RAM), or provided on anymedia capable of storing program code, such as any type of rotatingmedia including floppy disks, optical discs, digital video discs (DVDs),compact discs (CDs), micro-drives, magneto-optical discs, magnetic oroptical cards, nanosystems (including molecular memory integratedcircuits), or any other type of computer-readable medium or devicesuitable for storing instructions or data. Additionally, the entireprogram code, or portions thereof, may be transmitted and downloadedfrom a software source over a transmission medium, for example, over theInternet, or from another server, as is well known, or transmitted overany other existing network connection as is well known (for example,extranet, virtual private network (VPN), local area network (LAN), etc.)using any communication medium and protocols (for example, TCP/IP, HTTP,HTTPS, Ethernet, etc.) as are well known. It will also be appreciatedthat computer code for the disclosed implementations can be realized inany programming language that can be executed on a server or othercomputing system such as, for example, C, C++, HTML, any other markuplanguage, Java™, JavaScript, ActiveX, any other scripting language, suchas VB Script, and many other programming languages as are well known.

FIG. 1B illustrates a block diagram of example implementations ofelements of FIG. 1A and example interconnections between these elementsaccording to some implementations. That is, FIG. 1B also illustratesenvironment 10, but in FIG. 1B, various elements of database system 16and various interconnections between such elements are shown with morespecificity according to some more specific implementations. In someimplementations, database system 16 may not have the same elements asthose described herein or may have other elements instead of, or inaddition to, those described herein.

In FIG. 1B, user system 12 includes a processor system 12A, a memorysystem 12B, an input system 12C, and an output system 12D. The processorsystem 12A can include any suitable combination of one or moreprocessors. The memory system 12B can include any suitable combinationof one or more memory devices. The input system 12C can include anysuitable combination of input devices, such as one or more touchscreeninterfaces, keyboards, mice, trackballs, scanners, cameras, orinterfaces to networks. The output system 12D can include any suitablecombination of output devices, such as one or more display devices,printers, or interfaces to networks.

In FIG. 1B, network interface 20 is implemented as a set of HTTPapplication servers 100 ₁-100 _(N). Each application server 100, alsoreferred to herein as an “app server,” is configured to communicate withtenant database 22 and tenant data 23 stored therein, as well as systemdatabase 24 and system data 25 stored therein, to serve requestsreceived from user systems 12. Tenant data 23 can be divided intoindividual tenant storage spaces 112, which can be physically orlogically arranged or divided. Within each tenant storage space 112,tenant data 114 and application metadata 116 can similarly be allocatedfor each user. For example, a copy of a user's most recently used (MRU)items can be stored in tenant data 114. Similarly, a copy of MRU itemsfor an entire organization that is a tenant can be stored to tenantspace 112.

Database system 16 of FIG. 1B also includes a user interface (UI) 30 andan application programming interface (API) 32. Process space 28 includessystem process space 102, individual tenant process spaces 104 and atenant management process space 110. Application platform 18 includes anapplication setup mechanism 38 that supports application developers'creation and management of applications. Such applications and otherscan be saved as metadata into tenant database 22 by save routines 36 forexecution by subscribers as one or more tenant process spaces 104managed by tenant management process space 110, for example. Invocationsto such applications can be coded using procedural language forstructured query language (PL/SQL) 34, which provides a programminglanguage style interface extension to the API 32. A detailed descriptionof some PL/SQL language implementations is discussed in commonlyassigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWINGACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASESERVICE, issued on Jun. 1, 2010, and hereby incorporated by referenceherein in its entirety and for all purposes. Invocations to applicationscan be detected by one or more system processes, which manage retrievingapplication metadata 116 for the subscriber making the invocation andexecuting the metadata as an application in a virtual machine.

Each application server 100 can be communicably coupled with tenantdatabase 22 and system database 24, for example, having access to tenantdata 23 and system data 25, respectively, via a different networkconnection. For example, one application server 100 ₁ can be coupled viathe network 14 (for example, the Internet), another application server1002 can be coupled via a direct network link, and another applicationserver 100 _(N) can be coupled by yet a different network connection.Transfer Control Protocol and Internet Protocol (TCP/IP) are examples oftypical protocols that can be used for communicating between applicationservers 100 and database system 16. However, it will be apparent to oneskilled in the art that other transport protocols can be used tooptimize database system 16 depending on the network interconnectionsused.

In some implementations, each application server 100 is configured tohandle requests for any user associated with any organization that is atenant of database system 16. Because it can be desirable to be able toadd and remove application servers 100 from the server pool at any timeand for various reasons, in some implementations there is no serveraffinity for a user or organization to a specific application server100. In some such implementations, an interface system implementing aload balancing function (for example, an F5 Big-IP load balancer) iscommunicably coupled between application servers 100 and user systems 12to distribute requests to application servers 100. In oneimplementation, the load balancer uses a least-connections algorithm toroute user requests to application servers 100. Other examples of loadbalancing algorithms, such as round robin and observed-response-time,also can be used. For example, in some instances, three consecutiverequests from the same user could hit three different applicationservers 100, and three requests from different users could hit the sameapplication server 100. In this manner, by way of example, databasesystem 16 can be a multi-tenant system in which database system 16handles storage of, and access to, different objects, data, andapplications across disparate users and organizations.

In some embodiments, server 100 includes a transpiler, running in systemprocess space 102, for generating output source code (such as JavaScript(JS)) from input source code (such as formula syntax), the output sourcecode to be delivered via an application programming interface (API) toand be evaluated by one or more user systems 12.

In one example storage use case, one tenant can be a company thatemploys a sales force where each salesperson uses database system 16 tomanage aspects of their sales. A user can maintain contact data, leadsdata, customer follow-up data, performance data, goals and progressdata, etc., all applicable to that user's personal sales process (forexample, in tenant database 22). In an example of a MTS arrangement,because all of the data and the applications to access, view, modify,report, transmit, calculate, etc., can be maintained and accessed by auser system 12 having little more than network access, the user canmanage his or her sales efforts and cycles from any of many differentuser systems. For example, when a salesperson is visiting a customer andthe customer has Internet access in their lobby, the salesperson canobtain critical updates regarding that customer while waiting for thecustomer to arrive in the lobby.

While each user's data can be stored separately from other users' dataregardless of the employers of each user, some data can beorganization-wide data shared or accessible by several users or all ofthe users for a given organization that is a tenant. Thus, there can besome data structures managed database system 16 that are allocated atthe tenant level while other data structures can be managed at the userlevel. Because an MTS can support multiple tenants including possiblecompetitors, the MTS can have security protocols that keep data,applications, and application use separate. Also, because many tenantsmay opt for access to an MTS rather than maintain their own system,redundancy, up-time, and backup are additional functions that can beimplemented in the MTS. In addition to user-specific data andtenant-specific data, database system 16 also can maintain system leveldata usable by multiple tenants or other data. Such system level datacan include industry reports, news, postings, and the like that aresharable among tenants.

In some implementations, user systems 12 (which also can be clientsystems) communicate with application servers 100 to request and updatesystem-level and tenant-level data from database system 16. Suchrequests and updates can involve sending one or more queries to tenantdatabase 22 or system database 24. Database system 16 (for example, anapplication server 100 in database system 16) can automatically generateone or more SQL statements (for example, one or more SQL queries)designed to access the desired information. System database 24 cangenerate query plans to access the requested data from the database. Theterm “query plan” generally refers to one or more operations used toaccess information in a database system.

Each database can generally be viewed as a collection of objects, suchas a set of logical tables, containing data fitted into predefined orcustomizable categories. A “table” is one representation of a dataobject and may be used herein to simplify the conceptual description ofobjects and custom objects according to some implementations. It shouldbe understood that “table” and “object” may be used interchangeablyherein. Each table generally contains one or more data categorieslogically arranged as columns or fields in a viewable schema. Each rowor element of a table can contain an instance of data for each categorydefined by the fields. For example, a CRM database can include a tablethat describes a customer with fields for basic contact information suchas name, address, phone number, fax number, etc. Another table candescribe a purchase order, including fields for information such ascustomer, product, sale price, date, etc. In some MTS implementations,standard entity tables can be provided for use by all tenants. For CRMdatabase applications, such standard entities can include tables forcase, account, contact, lead, and opportunity data objects, eachcontaining pre-defined fields. As used herein, the term “entity” alsomay be used interchangeably with “object” and “table.”

In some MTS implementations, tenants are allowed to create and storecustom objects, or may be allowed to customize standard entities orobjects, for example by creating custom fields for standard objects,including custom index fields. Commonly assigned U.S. Pat. No.7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASESYSTEM, issued on Aug. 17, 2010, and hereby incorporated by referenceherein in its entirety and for all purposes, teaches systems and methodsfor creating custom objects as well as customizing standard objects in amulti-tenant database system. In some implementations, for example, allcustom entity data rows are stored in a single multi-tenant physicaltable, which may contain multiple logical tables per organization. It istransparent to customers that their multiple “tables” are in fact storedin one large table or that their data may be stored in the same table asthe data of other customers.

FIG. 2A shows a system diagram illustrating example architecturalcomponents of an on-demand database service environment 200 according tosome implementations. A client machine communicably connected with thecloud 204, generally referring to one or more networks in combination,as described herein, can communicate with the on-demand database serviceenvironment 200 via one or more edge routers 208 and 212. A clientmachine can be any of the examples of user systems 12 described above.The edge routers can communicate with one or more core switches 220 and224 through a firewall 216. The core switches can communicate with aload balancer 228, which can distribute server load over different pods,such as the pods 240 and 244. Pods 240 and 244, which can each includeone or more servers or other computing resources, can perform dataprocessing and other operations used to provide on-demand services.Communication with the pods can be conducted via pod switches 232 and236. Components of the on-demand database service environment cancommunicate with database storage 256 through a database firewall 248and a database switch 252.

As shown in FIGS. 2A and 2B, accessing an on-demand database serviceenvironment can involve communications transmitted among a variety ofdifferent hardware or software components. Further, the on-demanddatabase service environment 200 is a simplified representation of anactual on-demand database service environment. For example, while onlyone or two devices of each type are shown in FIGS. 2A and 2B, someimplementations of an on-demand database service environment can includeanywhere from one to many devices of each type. Also, the on-demanddatabase service environment need not include each device shown in FIGS.2A and 2B or can include additional devices not shown in FIGS. 2A and2B.

Additionally, it should be appreciated that one or more of the devicesin the on-demand database service environment 200 can be implemented onthe same physical device or on different hardware. Some devices can beimplemented using hardware or a combination of hardware and software.Thus, terms such as “data processing apparatus,” “machine,” “server,”“device,” and “processing device” as used herein are not limited to asingle hardware device; rather, references to these terms can includeany suitable combination of hardware and software configured to providethe described functionality.

Cloud 204 is intended to refer to a data network or multiple datanetworks, often including the Internet. Client machines communicablyconnected with cloud 204 can communicate with other components of theon-demand database service environment 200 to access services providedby the on-demand database service environment. For example, clientmachines can access the on-demand database service environment toretrieve, store, edit, or process information. In some implementations,edge routers 208 and 212 route packets between cloud 204 and othercomponents of the on-demand database service environment 200. Forexample, edge routers 208 and 212 can employ the Border Gateway Protocol(BGP). The BGP is the core routing protocol of the Internet. Edgerouters 208 and 212 can maintain a table of Internet Protocol (IP)networks or ‘prefixes,’ which designate network reachability amongautonomous systems on the Internet.

In some implementations, firewall 216 can protect the inner componentsof the on-demand database service environment 200 from Internet traffic.Firewall 216 can block, permit, or deny access to the inner componentsof on-demand database service environment 200 based upon a set of rulesand other criteria. Firewall 216 can act as one or more of a packetfilter, an application gateway, a stateful filter, a proxy server, orany other type of firewall.

In some implementations, core switches 220 and 224 are high-capacityswitches that transfer packets within the on-demand database serviceenvironment 200. Core switches 220 and 224 can be configured as networkbridges that quickly route data between different components within theon-demand database service environment. In some implementations, the useof two or more core switches 220 and 224 can provide redundancy orreduced latency.

In some implementations, pods 240 and 244 perform the core dataprocessing and service functions provided by the on-demand databaseservice environment. Each pod can include various types of hardware orsoftware computing resources. An example of the pod architecture isdiscussed in greater detail with reference to FIG. 2B. In someimplementations, communication between pods 240 and 244 is conducted viapod switches 232 and 236. Pod switches 232 and 236 can facilitatecommunication between pods 240 and 244 and client machines communicablyconnected with cloud 204, for example, via core switches 220 and 224.Also, pod switches 232 and 236 may facilitate communication between pods240 and 244 and database storage 256. In some implementations, loadbalancer 228 can distribute workload between pods 240 and 244. Balancingthe on-demand service requests between the pods can assist in improvingthe use of resources, increasing throughput, reducing response times, orreducing overhead. Load balancer 228 may include multilayer switches toanalyze and forward traffic.

In some implementations, access to database storage 256 is guarded by adatabase firewall 248. Database firewall 248 can act as a computerapplication firewall operating at the database application layer of aprotocol stack. Database firewall 248 can protect database storage 256from application attacks such as SQL injection, database rootkits, andunauthorized information disclosure. In some implementations, databasefirewall 248 includes a host using one or more forms of reverse proxyservices to proxy traffic before passing it to a gateway router.Database firewall 248 can inspect the contents of database traffic andblock certain content or database requests. Database firewall 248 canwork on the SQL application level atop the TCP/IP stack, managingapplications' connection to the database or SQL management interfaces aswell as intercepting and enforcing packets traveling to or from adatabase network or application interface.

In some implementations, communication with database storage 256 isconducted via database switch 252. Multi-tenant database storage 256 caninclude more than one hardware or software components for handlingdatabase queries. Accordingly, database switch 252 can direct databasequeries transmitted by other components of the on-demand databaseservice environment (for example, pods 240 and 244) to the correctcomponents within database storage 256. In some implementations,database storage 256 is an on-demand database system shared by manydifferent organizations as described above with reference to FIGS. 1Aand 1B.

FIG. 2B shows a system diagram further illustrating examplearchitectural components of an on-demand database service environmentaccording to some implementations. Pod 244 can be used to renderservices to a user of on-demand database service environment 200. Insome implementations, each pod includes a variety of servers or othersystems. Pod 244 includes one or more content batch servers 264, contentsearch servers 268, query servers 282, file servers 286, access controlsystem (ACS) servers 280, batch servers 284, and app servers 288. Pod244 also can include database instances 290, quick file systems (QFS)292, and indexers 294. In some implementations, some or allcommunication between the servers in pod 244 can be transmitted via podswitch 236.

In some implementations, app servers 288 include a hardware or softwareframework dedicated to the execution of procedures (for example,programs, routines, scripts) for supporting the construction ofapplications provided by on-demand database service environment 200 viapod 244. In some implementations, the hardware or software framework ofan app server 288 is configured to execute operations of the servicesdescribed herein, including performance of the blocks of various methodsor processes described herein. In some alternative implementations, twoor more app servers 288 can be included and cooperate to perform suchmethods, or one or more other servers described herein can be configuredto perform the disclosed methods.

In an embodiment, one or more transpilers are executed by app servers288 to transform input source code to output source code.

Content batch servers 264 can handle requests internal to the pod. Somesuch requests can be long-running or not tied to a particular customer.For example, content batch servers 264 can handle requests related tolog mining, cleanup work, and maintenance tasks. Content search servers268 can provide query and indexer functions. For example, the functionsprovided by content search servers 268 can allow users to search throughcontent stored in the on-demand database service environment. Fileservers 286 can manage requests for information stored in file storage298. File storage 298 can store information such as documents, images,and binary large objects (BLOBs). In some embodiments, file storage 298is a shared storage. By managing requests for information using fileservers 286, the image footprint on the database can be reduced. Queryservers 282 can be used to retrieve information from one or more filesystems. For example, query servers 282 can receive requests forinformation from app servers 288 and transmit information queries tonetwork file systems (NFS) 296 located outside the pod.

Pod 244 can share a database instance 290 configured as a multi-tenantenvironment in which different organizations share access to the samedatabase. Additionally, services rendered by pod 244 may call uponvarious hardware or software resources. In some implementations, ACSservers 280 control access to data, hardware resources, or softwareresources. In some implementations, batch servers 284 process batchjobs, which are used to run tasks at specified times. For example, batchservers 284 can transmit instructions to other servers, such as appservers 288, to trigger the batch jobs.

In some implementations, QFS 292 is an open source file system availablefrom Sun Microsystems, Inc. The QFS can serve as a rapid-access filesystem for storing and accessing information available within the pod244. QFS 292 can support some volume management capabilities, allowingmany disks to be grouped together into a file system. File systemmetadata can be kept on a separate set of disks, which can be useful forstreaming applications where long disk seeks cannot be tolerated. Thus,the QFS system can communicate with one or more content search servers268 or indexers 294 to identify, retrieve, move, or update data storedin NFS 296 or other storage systems.

In some implementations, one or more query servers 282 communicate withthe NFS 296 to retrieve or update information stored outside of the pod244. NFS 296 can allow servers located in pod 244 to access informationto access files over a network in a manner similar to how local storageis accessed. In some implementations, queries from query servers 282 aretransmitted to NFS 296 via load balancer 228, which can distributeresource requests over various resources available in the on-demanddatabase service environment. NFS 296 also can communicate with QFS 292to update the information stored on NFS 296 or to provide information toQFS 292 for use by servers located within pod 244.

In some implementations, the pod includes one or more database instances290. Database instance 290 can transmit information to QFS 292. Wheninformation is transmitted to the QFS, it can be available for use byservers within pod 244 without using an additional database call. Insome implementations, database information is transmitted to indexer294. Indexer 294 can provide an index of information available indatabase instance 290 or QFS 292. The index information can be providedto file servers 286 or QFS 292.

FIG. 3 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 300 within which a set ofinstructions (e.g., for causing the machine to perform any one or moreof the methodologies discussed herein) may be executed. In alternativeimplementations, the machine may be connected (e.g., networked) to othermachines in a LAN, a WAN, an intranet, an extranet, or the Internet. Themachine may operate in the capacity of a server or a client machine inclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PDA, acellular telephone, a web appliance, a server, a network router, switchor bridge, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Someor all of the components of the computer system 300 may be utilized byor illustrative of any of the electronic components described herein(e.g., any of the components illustrated in or described with respect toFIGS. 1A, 1B, 2A, and 2B).

The exemplary computer system 300 includes a processing device(processor) 302, a main memory 304 (e.g., ROM, flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM) or RambusDRAM (RDRAM), etc.), a static memory 306 (e.g., flash memory, staticrandom access memory (SRAM), etc.), and a data storage device 320, whichcommunicate with each other via a bus 310.

Processor 302 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, processor 302 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, or aprocessor implementing other instruction sets or processors implementinga combination of instruction sets. Processor 302 may also be one or morespecial-purpose processing devices such as an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like.Processor 302 is configured to execute instructions 326 for performingthe operations and steps discussed herein. Processor 302 may have one ormore processing cores.

Computer system 300 may further include a network interface device 308.Computer system 300 also may include a video display unit 312 (e.g., aliquid crystal display (LCD), a cathode ray tube (CRT), or a touchscreen), an alphanumeric input device 314 (e.g., a keyboard), a cursorcontrol device 316 (e.g., a mouse or touch screen), and a signalgeneration device 322 (e.g., a loud speaker).

Power device 318 may monitor a power level of a battery used to powercomputer system 300 or one or more of its components. Power device 318may provide one or more interfaces to provide an indication of a powerlevel, a time window remaining prior to shutdown of computer system 300or one or more of its components, a power consumption rate, an indicatorof whether computer system is utilizing an external power source orbattery power, and other power related information. In someimplementations, indications related to power device 318 may beaccessible remotely (e.g., accessible to a remote back-up managementmodule via a network connection). In some implementations, a batteryutilized by power device 318 may be an uninterruptable power supply(UPS) local to or remote from computer system 300. In suchimplementations, power device 318 may provide information about a powerlevel of the UPS.

Data storage device 320 may include a computer-readable storage medium324 (e.g., a non-transitory computer-readable storage medium) on whichis stored one or more sets of instructions 326 (e.g., software)embodying any one or more of the methodologies or functions describedherein. Instructions 326 may also reside, completely or at leastpartially, within main memory 304 and/or within processor 302 duringexecution thereof by computer system 300, main memory 304, and processor302 also constituting computer-readable storage media. Instructions 326may further be transmitted or received over a network 330 (e.g., network14) via network interface device 308.

In one implementation, instructions 326 include instructions forperforming any of the implementations described herein. Whilecomputer-readable storage medium 324 is shown in an exemplaryimplementation to be a single medium, it is to be understood thatcomputer-readable storage medium 324 may include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) that store the one or more sets ofinstructions.

FIG. 4 illustrates an example of a user interface 400 to input dataprocessing commands resulting in a formula in some embodiments. In oneembodiment, user interface 400 is a part of application platform 18 ofFIG. 1A. In another embodiment, user interface 400 is a part of usersystem 12. A user (such as a system administrator, for example), maydesire to enter and/or generate an input source code (for example,written in a formula language) to manage processing by database system16. In one embodiment, the input source code is generated manually by asystem administrator or other user by typing in the formula according tothe language syntax. In another embodiment, at least a portion of theinput source code is automatically produced by selection ofpredetermined fields in user interface 400 and automatic generation ofinput source code from those selections. For example, a user may makevarious selections in user interface window 402 such as filter type 404,field 406, operator 408, and value 410, and filters window 412 such asamount greater than or equal field 414. These fields are only examplesand any fields in a UI may be used that result in automatic generationof input source code (e.g., written in a formula language) that conformsto the formula language syntax, grammar and/or rules. Thus, userinterface 400 generates input source code representing the selections.

FIG. 5 illustrates an example 500 of an application platform 18 havingan optimizing transpiler system 503 to transform input source code 502into output source code 514 in some embodiments. Input source code 502(whether manually generated or automatically generated) is input toparser 504 of optimizing transpiler system 503. In one embodiment, inputsource code 502 is in the syntax of a formula language commerciallyavailable from Salesforce.Inc. In other embodiments, other languages maybe used for the input source code, such as JavaScript, C++,Coffeescript, Dart, and Hypertext Preprocessor (PHP). Parser 504analyzes input source code 502 and in at least one embodiment generatesan abstract syntax tree (AST) representing the input source code. An ASTis a tree representation of the abstract syntactic structure of sourcecode written in a programming language. Each node of the tree denotes aconstruct occurring in the source code. The syntax is “abstract” in thesense that the AST does not represent every detail appearing in the realsyntax, but rather just the structural or content-related details. Forinstance, grouping parentheses are implicit in the tree structure, sothese do not have to be represented as separate nodes. Likewise, asyntactic construct like an if-condition-then expression may be denotedby means of a single node with three branches. In other embodiments,other data structures may be used instead of an AST, including otherkinds of trees.

In embodiments of the present invention, AST 506 is input to optimizer508, which traverses the AST 506, analyzes the structure of the AST,analyzes each node of the AST, and optimizes the AST into an optimizedAST and zero or more associated sub-ASTs 510 (e.g., sub-trees).Transpiler 512 transpiles the input source code as represented by theoptimized AST and the zero or more associated sub-ASTS 510 to produceoutput source code 514 written in a selected programming language thatis smaller than the output of the transpiler would be without the use ofoptimizer 508. In one embodiment, output source code 514 is written inJavaScript. In other embodiments, other languages may be used, such asC, C++, and structured query language (SQL). Output source code 514 maythen be communicated to user system 12 for evaluation, interpretation,compilation and/or execution by, for example, compiler 518 and/or userinterface 518. User system 12 may use additional data to evaluate,interpret, compile and/or execute the output source code.

FIG. 6 illustrates an example 600 of a first formula as source inputcode. In this simple example, the first formula 600 (also called an“original” formula) is written in a first language such as a formulalanguage as shown. In the example, the first formula is the expression“if (a=0, b+c, (b+c)/a).” First formula 600 is parsed by parser 504 togenerate an AST.

FIG. 7 illustrates an example of AST 700 generated for the first formula600 by parser 504. AST 700 includes a root node 702 for the “if”operator. Root node 702 includes three child nodes 704, 710, and 716,each child node being the root node of a sub-tree of AST 700. Firstchild node 704 includes the “=” operator and includes two child nodes ofits own, node 706 (including an “a” variable) and node 708 (including a“0” value). Second child node 710 includes the “+” operator and includestwo child nodes of its own, node 712 (including a “b” variable) and node714 (including a “c” variable). Third child node 716 includes the “/”operator and includes two child nodes of its own, node 718 (includingthe “+” operator) and node 720 (including the “a” variable). Node 718 isalso root node of a sub-tree, having two child nodes 722 (including the“b” variable) and 724 (including the “c” variable).

FIG. 8 illustrates an example of an AST 800 for first formula 600including generated output source code. In this example, transpiler 512operates on (unoptimized) AST 700 and produces the resulting sourceoutput code shown in FIG. 8 as appended (for demonstration purposes) toassociated nodes of AST 800. For example, starting with the root node802 of AST 700, root node 802 includes output source code of “if(a!=null?a==0:false)?(b!=null&&c!=null:b+c:null)((b!=null&&c!=null:b+c:null)!=null&&a!=null?(b!=null&&c!=null:b+c:null)/a:null).”Node 804 includes output source code of “a!null?a==0:false.” Node 806includes “a.” Node 808 includes “0.” Node 810 includes“b!=null&&c!=null:b+c:null.” Node 812 includes “b.” Node 814 includes“c.” Node 816 includes“(b!=null&&c!=null:b+c:null)!=null&&a!=null?(b!=null&&c!=null:b+c:null)/a:null.”Node 818 includes “b!=null&&c!=null:b+c:null.” Node 820 includes “b.”Finally, node 822 includes “c.”

Thus, the final output source code (unoptimized version) contains 140characters (e.g., for a JavaScript implementation) “letresult=(a!=null?a==0:false)?(b!=null&&c!=null:b+c:null):((b!=null&&c!=null:b+c:null)!=null&&a!=null?(b!=null&&c!null:b+c:null)/a:null).” These 140 characters canbe sent to user system 12 for subsequent processing.

In embodiments of the present invention, optimizer 508 analyzes AST 700to identify and apply optimizations to AST 700 to result in transpiler512 generating smaller and more efficient output source code based onthe optimized AST. For example, optimizer 508, when analyzing AST 700,determines that there is a duplicate sub-tree representing theexpression “b+c.” This duplicate sub-tree shows up in AST 700 as nodes710, 712, and 714, and as nodes 718, 722, and 724.

FIG. 9 illustrates an example of a sub-AST 900 according to someembodiments. In this example, optimizer 508 defines a new uniquevariable “x” to represent a sub-tree of AST 700, called a sub-ASTherein, having a root node 902 including the “+” operator, and two childnodes, node 904 including the “b” variable and node 906 including the“c” variable. Optimizer 508 logically replaces occurrences of thesub-AST 900 for the expression “b+c” with variable “x” wherever thesub-tree occurs in AST 700, thereby generating an optimized AST.

FIG. 10 illustrates an example of an optimized AST 1000 for firstformula 600 according to some embodiments. Optimized AST 1000 includesroot node 1002 including the “if operator. Root node 1002 has threechild nodes 1004, 1010, and 1012. Node 1004 includes the “=” operatorand two child nodes, node 1006 (including the “a” variable) and node1008 (including the “0” value). Node 1010 includes the “x” variable(replacing the expression “b+c” of the original, unoptimized AST with asub-AST for “x”). Node 1012 includes the “I” operator and two childnodes, node 1014 (including the “x” variable replacing the expression“b+c”) and node 1016 (including the “a” variable).

FIG. 11 illustrates an example of an optimized AST 1100 for firstformula 600 including generated output source code according to someembodiments. In this example, transpiler 512 operates on optimized AST1000 and produces the resulting source output code shown in FIG. 11 asappended (for demonstration purposes) to associated nodes of AST 1100.For example, root node 1102 includes the “if” operator and“(a!=null?a==0:false)?(x):x!=null&&a!null?x/a:null).” Node 1104 includesthe “=” operator and “a!=null?a==0:false.” Node 1106 includes the “a”variable. Node 1108 includes the “0” value. Node 1110 includes the “x”variable. Node 1112 includes the “I” operator and“x!=null&&a!=null?x/a:null.” Node 1114 includes the “x” variable andnode 1116 includes the “a” variable.

Thus, the final output source code (optimized version) contains 121characters (e.g., for a JavaScript implementation)=

“let x=b!=null&&c!null:b+c:null:”

“let result=(a!=null?a==0:false)?(x):x!=null&&a!=null?x/a:null).”

These 61 characters can be sent to user system 12 for subsequentprocessing. By using optimizer 508, the resulting output source code hasbeen shrunk by approximately 56%.

FIG. 12 illustrates an example of a second formula 1200 as source inputcode. In this more complicated example, the second formula 1200 (alsocalled an “original” formula) is written in a first language such as aformula language as shown. Second formula 1200 is parsed by parser 504to generate an AST.

FIG. 13 illustrates an example of an AST 1300 for second formula 1200.AST 1300 is a data structure representation of original formula 1200generated by parser 504. AST 1300 includes a root node 1302 for the “if”operator. Root node 1302 includes three child nodes 1304, 1310, and1312. First child node 1304 includes the “=” operator and includes twochild nodes of its own, node 1306 (including an “a” variable) and node1308 (including a “0” value). Second child node 1310 includes the “1”value. Third child node 1312 is root node for a sub-tree for the“ceiling” operator. Node 1312 includes a child node 1314 including the“+” operator. Node 1314 includes two child nodes 1316 and 1322. Node1316 includes the “I” operator and two child nodes 1318 and 1320. Node1318 includes the “b” variable. Node 1320 includes the “a” variable.Node 1322 includes the “floor” operator and child node 1324. Node 1324includes the “I” operator and two child nodes 1326 and 1328. Node 1326includes the “b” variable and node 1328 includes the “a” variable.

If generated (unoptimized) AST 1300 is submitted to transpiler 512,transpiler will generate 926 characters of resulting output source codeas follows:

“Letresult=(a!=null?a==0:false)?(1):(((b!=null&&a!=null?b/a:null)!=null&&(((b!=null&&a!=null?b/a:null)>=0?Math.floor((b!=null&&a!=null?b/a:null)):Math.ceil((b!=null&&a!=null?b/a:null))))!=null?(b!=null&&a!=null?b/a:null)+(((b!=null&&a!=null?b/a:null)>=0?Math.floor((b!=null&&a!=null?b/a:null)):Math.ceil((b!=null&&a!=null?b/a:null)))):null)>=0?Math.ceil(((b!=null&&a!=null?b/a:null)!=null&&(((b!=null&&a!=null?b/a:null)>=0?Math.floor((b!=null&&a!=null?b/a: null)):Math.ceil((b!=null&&a!=null?b/a:null))))!=null?(b!=null&&a!=null?b/a!=null)+(((b!=null&&a!=null?b/a!=null)>=0?Math.floor((b!=null&&a!=null?b/a:null)):Math.ceil((b!=null&&a!=null?b/a:null)))):null)):Math.floor(((b!=null&&a!=null?b/a:null)!=null&&(((b!=null&&a!=null?b/a:null)>=0?Math.floor((b!=null&&a!=null?b/a:null)):Math.ceil((b!=null&&a!=null?b/a:null))))!=null?(b!=null&&a!=null?b/a:null)+(((b!=null&&a!=null?b/a:null)>=0?Math.floor((b!=null&&a!=null?b/a:null)):Math.ceil((b!=null&&a!=null?b/a:null)))):null))).”

FIG. 14 illustrates examples of sub-ASTs 1400 according to someembodiments. In this example, optimizer 508 defines a new uniquevariable “x” to represent a sub-AST, having a root node 1402 includingthe “/” operator, and two child nodes, node 1404 including the “b”variable and node 1406 including the “a” variable. Optimizer 508logically replaces occurrences of the sub-AST for the expression “b+a”with variable “x” wherever the sub-AST occurs in AST 1300, therebygenerating an optimized AST. In this example, optimizer 508 defines anew variable “y” to represent a sub-AST, having a root node 1408including the “floor” operator and one child node 1410 including the “x”variable. Optimizer 508 logically replaces occurrences of the sub-ASTfor the expression “floor(x)” with variable “y” wherever the sub-ASToccurs in AST 1300. In this example, optimizer 508 defines a new uniquevariable “z” to represent a sub-AST, having a root node 1412 includingthe “+” operator, and two child nodes, node 1414 including the “x”variable and node 1416 including the “y” variable. Optimizer 508logically replaces occurrences of the sub-AST for the expression “x+y”with variable “z” wherever the sub-tree occurs in AST 1300.

FIG. 15 illustrates an example of an optimized AST 1500 for secondformula 1200 according to some embodiments. Optimized AST 1500 includesroot node 1502 including the “if operator. Root node 1502 has threechild nodes 1504, 1510, and 1512. Node 1504 includes the “=” operatorand two child nodes, node 1506 (including the “a” variable) and node1508 (including the “0” value). Node 1510 includes the “1” value. Node1512 includes the “ceiling” operator and one child node 1514. Child node1514 includes the “z” variable.

FIG. 16 illustrates an example of an optimized AST 1600 for secondformula 1200 including generated output source code according to someembodiments. In this example, transpiler 512 operates on optimized AST1500 and produces the resulting source output code shown in FIG. 16 asappended (for demonstration purposes) to associated nodes of AST 1600.For example, root node 1602 includes the “if” operator and “if(a!=null?a==0:false)?(1):(z>0?math.ceil(z):math.floor(z)).” Node 1604includes the “=” operator and “a!=null?a==0:false.” Node 1606 includesthe “a” variable. Node 1608 includes the “0” value. Node 1610 includesthe “1” value. Node 1612 includes the “ceiling” operator and“(z>0?math.ceil(z):math.floor(z)).” Node 1614 includes the “z” variable.

Thus, the final output source code (optimized version) contains 180characters (e.g., for a JavaScript implementation)=

“let x=b!null&&a!null?b/a:null;

let y=(x>0?math.floor(x):math.ceil(x));

let z=x!null&&y!null?x+y:null;

let result=(a!null?a==0:false)?(1):z>=0?math.ceil(z):math.floor(z)).”

These 180 characters can be sent to user system 12 for subsequentprocessing. By using optimizer 508, the resulting output source code hasbeen shrunk by approximately 81%.

FIG. 17 is a flow diagram of an example process 1700 for optimizing anAST according to some embodiments. In an embodiment, this processing isperformed by optimizer 508. At block 1702, optimizer traverses the AST506 representing input source code 502 to identify a reusable sub-AST ofAST 506. If a reusable sub-AST is found at block 1704, optimizer 508replaces the reusable sub-AST in the AST with a variable at block 1706.If a reusable sub-AST is not found at block 1704, processing ends atblock 1710. At block 1708, if traversal of the AST is done, thenoptimization processing is done at block 1710. If traversal of the ASTis not done, further traversal continues. In an embodiment, traversal ofthe AST is performed recursively starting from the root node of the AST.

FIG. 18 is a flow diagram of an example process 1800 for optimizing anAST according to some embodiments. In an embodiment, this processing isperformed by optimizer 508. Process 1800 provides more details thanprocess 1700. At block 1802, optimizer 508 traverses AST 506 andannotates each node in the AST with a hash value representing a sub-ASTof the node. The hash value serves as an indicator of the sub-AST. In anembodiment, traversal of the AST is performed recursively. At block1804, optimizer 508 traverses AST 506 and annotates each node in the ASTwith the level of the node in the AST. In an embodiment, the level isrepresented by a natural number (e.g., this signifies the distance downthe AST from the root node). Next, optimizer 508 will traverse the ASTto identify reusable sub-ASTs. At block 1806, optimizer 508 sets acurrent node variable to the root node of the AST. The current nodevariable is used to keep track of where in the AST optimizer processingis currently operating.

In one embodiment, a data structure called a set of visited nodes isused to store information regarding traversal of the AST. Each entry inthe set of visited nodes indicates that an identified node has alreadybeen visited during traversal of the AST. In one embodiment, a datastructure called a set of replaced sub-ASTs is used to store informationregarding optimization of the AST. Each entry in the set of replacedsub-ASTs indicates that an identified node (and the node's sub-tree) hasbeen replaced by a variable.

At block 1808, if the current node is equivalent to one of the nodes inthe set of replaced sub-ASTs, then the contents of the current node havealready been encountered during earlier (e.g., higher) traversal of AST.The earlier encounter resulted in replacing the node with a variable. Atblock 1814, optimizer 508 replaces the current node in the AST (and thenode's sub-tree) with a new leaf node with the variable name of theearlier replaced sub-AST. Optimizer processing continues with furthertraversal of the AST by setting the current node to the next node in theAST at block 1815. In an embodiment, the next node is the node in theAST to be visited as part of recursive traversal of the AST (e.g., asubsequent node on the same level of the AST as the current node, or thefirst node of the next level of the AST).

At block 1808, if the current node is not equivalent to any node in theset of replaced sub-ASTs, processing proceeds to block 1810, whereoptimizer 508 determines if the current node is in the set of visitednodes (e.g., meaning the optimizer has previously processed a node withthe same contents earlier in the AST traversal). If the current nodedoes not match or is not equivalent to is any node in the set of visitednodes, this is the first time this node has been encountered during ASTtraversal. Optimizer 508 then adds the current node to the set ofvisited nodes at block 1812. Processing continues by recursivelyprocessing each child node of the current node. This involves settingthe current node to a child node and changing program control back toblock 1808 for processing of the child node.

At block 1810, if the current node is in the set of visited nodes, thecontents of the current node have already been encountered earlierduring the traversal of the AST. In that case, at block 1816 optimizer508 assigns a new unique variable name to the current node (e.g., avariable name not already stored in the set of replaced sub-ASTs). Atblock 1818, optimizer 508 stores the new variable name in the set ofreplaced sub-ASTs as a newly replaced sub-AST. At block 1820, optimizer508 replaces the current sub-AST (with the current node as its rootnode) with a leaf node referencing the new variable. At block 1822,optimizer 508 replaces the previous instance of the current node in theAST with a leaf node referencing the new variable. Thus, there have beentwo replacements—one at the current node and one at the previous nodethat was passed that is equal to the current node. In one embodiment,optimizer 508 optionally removes the current node from the set ofvisited nodes to improve the speed at which future nodes may be found.Processing continues by recursively processing each child node of thecurrent node. This involves setting the current node to a child node andchanging program control back to block 1808 for processing of the childnode. Processing completes when recursive traversal of the AST reachesevery node.

FIG. 19 is a flow diagram of an example process 900 for transpilinginput source code represented by an optimized AST and sub-ASTs 510according to some embodiments. At block 1902, transpiler 512 sorts theset of replaced sub-ASTs by level in AST 506. In one embodiment, thesorting of the replaced sub-ASTs reorders the replaced sub-ASTs fromdeepest level of the AST to top-most level of the AST. This ensures thatthe last/innermost sub-ASTs (e.g., deeper nodes in the AST) areprocessed first. At block 1904, transpiler 512 sets a current sub-AST tothe first replaced sub-AST in the now sorted set of replaced sub-ASTs.At block 1906, transpiler 512 transpiles the current sub-AST. At block1908, transpiler 512 appends the source code generated for the currentsub-AST as an assignment state to the variable name assigned to thecurrent sub-AST in the set of replaced sub-ASTs. At block 1910,transpiler 512 determines if all replaced sub-ASTs have been processed.If not, transpiler 512 sets the current sub-AST to the next sub-AST inthe set of replaced sub-ASTs and processing continues with block 1906.If all replaced sub-ASTs have been processed, transpiler 512 transpilesthe optimized AST 510 into output source code 514. At block 1916,transpiler 512 returns the output source code 514 as assigned to aresult variable.

Examples of systems, apparatuses, computer-readable storage media, andmethods according to the disclosed implementations are described in thissection. These examples are being provided solely to add context and aidin the understanding of the disclosed implementations. It will thus beapparent to one skilled in the art that the disclosed implementationsmay be practiced without some or all of the specific details provided.In other instances, certain process or method operations, also referredto herein as “blocks,” have not been described in detail in order toavoid unnecessarily obscuring the disclosed implementations. Otherimplementations and applications also are possible, and as such, thefollowing examples should not be taken as definitive or limiting eitherin scope or setting.

In the detailed description, references are made to the accompanyingdrawings, which form a part of the description and in which are shown,by way of illustration, specific implementations. Although thesedisclosed implementations are described in sufficient detail to enableone skilled in the art to practice the implementations, it is to beunderstood that these examples are not limiting, such that otherimplementations may be used and changes may be made to the disclosedimplementations without departing from their spirit and scope. Forexample, the blocks of the methods shown and described herein are notnecessarily performed in the order indicated in some otherimplementations. Additionally, in some other implementations, thedisclosed methods may include more or fewer blocks than are described.As another example, some blocks described herein as separate blocks maybe combined in some other implementations. Conversely, what may bedescribed herein as a single block may be implemented in multiple blocksin some other implementations. Additionally, the conjunction “or” isintended herein in the inclusive sense where appropriate unlessotherwise indicated; that is, the phrase “A, B, or C” is intended toinclude the possibilities of “A,” “B,” “C,” “A and B,” “B and C,” “A andC,” and “A, B, and C.”

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example” or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion.

In addition, the articles “a” and “an” as used herein and in theappended claims should generally be construed to mean “one or more”unless specified otherwise or clear from context to be directed to asingular form. Reference throughout this specification to “animplementation,” “one implementation,” “some implementations,” or“certain implementations” indicates that a particular feature,structure, or characteristic described in connection with theimplementation is included in at least one implementation. Thus, theappearances of the phrase “an implementation,” “one implementation,”“some implementations,” or “certain implementations” in variouslocations throughout this specification are not necessarily allreferring to the same implementation.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the manner used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is herein, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, or otherwise manipulated. It has provenconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “receiving,” “retrieving,” “transmitting,” “computing,”“generating,” “adding,” “subtracting,” “multiplying,” “dividing,”“optimizing,” “calibrating,” “detecting,” “performing,” “analyzing,”“determining,” “enabling,” “identifying,” “modifying,” “transforming,”“applying,” “aggregating,” “extracting,” “registering,” “querying,”“populating,” “hydrating,” “updating,” or the like, refer to the actionsand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(e.g., electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission, or display devices.

The specific details of the specific aspects of implementationsdisclosed herein may be combined in any suitable manner withoutdeparting from the spirit and scope of the disclosed implementations.However, other implementations may be directed to specificimplementations relating to each individual aspect, or specificcombinations of these individual aspects. Additionally, while thedisclosed examples are often described herein with reference to animplementation in which a computing environment is implemented in asystem having an application server providing a front end for anon-demand database service capable of supporting multiple tenants, thepresent implementations are not limited to multi-tenant databases ordeployment on application servers. Implementations may be practicedusing other database architectures, i.e., ORACLE®, DB2® by IBM, and thelike without departing from the scope of the implementations claimed.Moreover, the implementations are applicable to other systems andenvironments including, but not limited to, client-server models, mobiletechnology and devices, wearable devices, and on-demand services.

It should also be understood that some of the disclosed implementationscan be embodied in the form of various types of hardware, software,firmware, or combinations thereof, including in the form of controllogic, and using such hardware or software in a modular or integratedmanner. Other ways or methods are possible using hardware and acombination of hardware and software. Any of the software components orfunctions described in this application can be implemented as softwarecode to be executed by one or more processors using any suitablecomputer language such as, for example, C, C++, Java™ (a trademark ofSun Microsystems, Inc.), or Perl using, for example, existing orobject-oriented techniques. The software code can be stored asnon-transitory instructions on any type of tangible computer-readablestorage medium (referred to herein as a “non-transitorycomputer-readable storage medium”). Examples of suitable media includerandom access memory (RAM), read-only memory (ROM), magnetic media suchas a hard-drive or a floppy disk, or an optical medium such as a compactdisc (CD) or digital versatile disc (DVD), flash memory, and the like,or any combination of such storage or transmission devices.Computer-readable media encoded with the software/program code may bepackaged with a compatible device or provided separately from otherdevices (for example, via Internet download). Any such computer-readablemedium may reside on or within a single computing device or an entirecomputer system and may be among other computer-readable media within asystem or network. A computer system, or other computing device, mayinclude a monitor, printer, or other suitable display for providing anyof the results mentioned herein to a user.

The disclosure also relates to apparatuses, devices, and systemadapted/configured to perform the operations herein. The apparatuses,devices, and systems may be specially constructed for their requiredpurposes, may be selectively activated or reconfigured by a computerprogram, or some combination thereof.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. While specific implementations have beendescribed herein, it should be understood that they have been presentedby way of example only, and not limitation. The breadth and scope of thepresent application should not be limited by any of the implementationsdescribed herein but should be defined only in accordance with thefollowing and later-submitted claims and their equivalents. Indeed,other various implementations of and modifications to the presentdisclosure, in addition to those described herein, will be apparent tothose of ordinary skill in the art from the foregoing description andaccompanying drawings. Thus, such other implementations andmodifications are intended to fall within the scope of the presentdisclosure.

Furthermore, although the present disclosure has been described hereinin the context of a particular implementation in a particularenvironment for a particular purpose, those of ordinary skill in the artwill recognize that its usefulness is not limited thereto and that thepresent disclosure may be beneficially implemented in any number ofenvironments for any number of purposes. Accordingly, the claims setforth below should be construed in view of the full breadth and spiritof the present disclosure as described herein, along with the full scopeof equivalents to which such claims are entitled.

1. An apparatus, comprising: a processing device; and a memory devicecoupled to the processing device, the memory device having instructionsstored thereon that, in response to execution by the processing device,cause the processing device to: parse input source code and generate atree representing the input source code; optimize the tree byrecursively traverse the tree starting with a root node of the tree as acurrent node; if the current node represents a reusable sub-tree alreadyencountered during traversal, replace the current node with a first leafnode assigned to a variable; and if the current node does not representa reusable sub-tree already encountered during traversal, and thecurrent node has already been encountered during traversal, assign a newvariable, replace the current node with a second leaf node referencingthe new variable, and replace a previous instance of the current nodewith a third leaf node referencing the new variable; and transpile theoptimized tree to generate output source code.
 2. The apparatus of claim1, wherein the tree is an abstract syntax tree.
 3. The apparatus ofclaim 1, wherein the input source code is in a formula language.
 4. Theapparatus of claim 1, wherein the output source code in JavaScript. 5.(canceled)
 6. The apparatus of claim 1, wherein instructions to optimizethe tree comprise instructions that, in response to execution by theprocessing device, cause the processing device to: if the current nodedoes not represent a reusable sub-tree already encountered duringtraversal, and the current node has already been encountered duringtraversal, store the new variable in a set of replaced sub-trees.
 7. Theapparatus of claim 1, wherein instructions to optimize the tree compriseinstructions that, in response to execution by the processing device,cause the processing device to: if the current node does not represent areusable sub-tree already encountered during traversal, and the currentnode has not already been encountered during traversal, add the currentnode to a set of nodes already encountered during traversal.
 8. Theapparatus of claim 1, wherein instructions to transpile the optimizedtree to generate output source code comprise instructions that, inresponse to execution by the processing device, cause the processingdevice to: sort replaced sub-trees by level of the tree; and transpile,in sorted order, each replaced sub-tree, and append generated sourcecode for each replaced sub-tree as an assignment statement to a variableassigned to each replaced sub-tree.
 9. The apparatus of claim 8,comprising instructions that, in response to execution by the processingdevice, cause the processing device to: sort the replaced sub-trees bylevel of the tree from deepest level of the tree to top-most level ofthe tree.
 10. A computer-implemented method comprising: parsing inputsource code and generating a tree representing the input source code;optimizing the tree by recursively traversing the tree starting with aroot node of the tree as a current node, if the current node representsa reusable sub-tree already encountered during traversal, replacing thecurrent node with a first leaf node assigned to a variable, and if thecurrent node does not represent a reusable sub-tree already encounteredduring traversal, and the current node has already been encounteredduring traversal, assigning a new variable, replacing the current nodewith a second leaf node referencing the new variable, and replacing aprevious instance of the current node with a third leaf node referencingthe new variable; and transpiling the optimized tree to generate outputsource code.
 11. The computer-implemented method of claim 10, whereinthe tree is an abstract syntax tree.
 12. The computer-implemented methodof claim 10, wherein the input source code is in a formula language. 13.The computer-implemented method of claim 10, wherein the output sourcecode in JavaScript.
 14. (canceled)
 15. The computer-implemented methodof claim 10, wherein optimizing the tree comprises: if the current nodedoes not represent a reusable sub-tree already encountered duringtraversal, and the current node has already been encountered duringtraversal, storing the new variable in a set of replaced sub-trees. 16.The computer-implemented method of claim 10, wherein optimizing the treecomprises: if the current node does not represent a reusable sub-treealready encountered during traversal, and the current node has notalready been encountered during traversal, adding the current node to aset of nodes already encountered during traversal.
 17. Thecomputer-implemented method of claim 10, wherein transpiling theoptimized tree to generate output source code comprises: sortingreplaced sub-trees by level of the tree; and transpiling, in sortedorder, each replaced sub-tree, and appending generated source code foreach replaced sub-tree as an assignment statement to a variable assignedto each replaced sub-tree.
 18. The computer-implemented method of claim17, comprising sorting the replaced sub-trees by level of the tree fromdeepest level of the tree to top-most level of the tree.
 19. A tangible,non-transitory computer-readable storage medium having instructionsencoded thereon which, when executed by a processing device, cause theprocessing device to: parse input source code and generating a treerepresenting the input source code; optimize the tree by recursivelytraverse the tree starting with a root node of the tree as a currentnode; if the current node represents a reusable sub-tree alreadyencountered during traversal, replace the current node with a first leafnode assigned to a variable; and if the current node does not representa reusable sub-tree already encountered during traversal, and thecurrent node has already been encountered during traversal, assign a newvariable, replace the current node with a second leaf node referencingthe new variable, and replace a previous instance of the current nodewith a third leaf node referencing the new variable; and transpile theoptimized tree to generate output source code.
 20. (canceled)
 21. Thetangible, non-transitory computer-readable storage medium of claim 19,wherein instructions stored thereon to optimize the tree compriseinstructions that, in response to execution by the processing device,cause the processing device to: if the current node does not represent areusable sub-tree already encountered during traversal, and the currentnode has already been encountered during traversal, store the newvariable in a set of replaced sub-trees.
 22. The tangible,non-transitory computer-readable storage medium of claim 19, whereininstructions stored thereon to optimize the tree comprise instructionsthat, in response to execution by the processing device, cause theprocessing device to: if the current node does not represent a reusablesub-tree already encountered during traversal, and the current node hasnot already been encountered during traversal, add the current node to aset of nodes already encountered during traversal.
 23. The tangible,non-transitory computer-readable storage medium of claim 19, whereininstructions stored thereon to transpile the optimized tree to generateoutput source code comprise instructions that, in response to executionby the processing device, cause the processing device to: sort replacedsub-trees by level of the tree; and transpile, in sorted order, eachreplaced sub-tree, and append generated source code for each replacedsub-tree as an assignment statement to a variable assigned to eachreplaced sub-tree.
 24. The tangible, non-transitory computer-readablestorage medium of claim 23, comprising instructions stored thereon that,in response to execution by the processing device, cause the processingdevice to: sort the replaced sub-trees by level of the tree from deepestlevel of the tree to top-most level of the tree.